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| K-L Divergence Based Model Clustering Method for Fast Speaker Identification |
| WANG Huan-Liang1,2,HAN Ji-Qing1,ZHENG Gui-Bin1 |
1.School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001
2.College of Information Science and Technology,Qingdao University of Science and Technology,Qingdao 266035 |
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Abstract With the increase of enrolled speakers and audio data to be recognized, the conventional speaker identification methods can not meet the real-time demand for internet application environment. A K-L divergence based speaker model clustering method is proposed to construct a hierarchical identification system, which remarkably improves the recognition efficiency. Moreover, the confidence measure using class-level identification information is also investigated to effectively exclude out-of-set speaker as early as possible. The experimental results show the proposed method averagely increases the identification speed by 3.2 times while the error rate of closed-set identification only increases about 0.9% compared with the conventional method. The open-set identification can be speeded up by using class-level confidence measure and a relatively 5.1% error rate reduction can be achieved on out-of-set speakers identification while keeping the identification performance of in-set speakers unchanged.
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Received: 09 February 2009
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2010, Vol. 23 
